MétaCan
Menu
Back to cohort
Record W1601460212

Bases demographiques de la montee de l'emploi et des gains chez les meres seules au Canada et aux Etats-Unis, 1980 a 2000

2008· preprint· fr· W1601460212 on OpenAlexaffabout
Feng Hou, Karen Myers, John Myles, Garnett Picot

Bibliographic record

VenueRePEc: Research Papers in Economics · 2008
Typepreprint
Languagefr
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsHumanitiesPolitical scienceArt
DOInot available

Abstract

fetched live from OpenAlex

Malgre des reformes relativement modestes du regime de bien-etre au Canada par rapport aux Etats Unis, les taux d'emploi et la remuneration des meres seules ont progresse depuis 1980 presque d'un meme ordre de grandeur dans ces deux pays. Nous allons montrer que la plupart des hausses au Canada et une partie appreciable de l'evolution aux Etats-Unis s'expliquent par la dynamique de la succession des cohortes et du vieillissement de la population, la generation nombreuse et plus scolarisee du baby-boom ayant remplace les anciennes cohortes et ayant elle-meme accede a la quarantaine. Au Canada comme aux Etats-Unis, les effets demographiques sont le principal facteur expliquant la montee de l'emploi et des gains chez les meres seules plus agees (40 ans et plus). Chez les meres plus jeunes en revanche, ce qui a surtout joue, c'est l'evolution du comportement sur le marche du travail et d'autres variables non mesurees. Dans l'ensemble, l'evolution demographique a predomine au Canada, mais non aux Etats-Unis, et ce, pour deux raisons : a) les meres seules sont bien plus agees au Canada qu'aux Etats-Unis; b) dans le sens meme de la these de la reforme du bien-etre, l'evolution des comportements a eu bien plus d'ampleur chez les meres seules plus jeunes aux Etats-Unis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.190
GPT teacher head0.437
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicRetirement, Disability, and EmploymentFrench-language works237,207